Evidence map›Paper›PMID 36947034›Full record

ArticleJournal of insect science (Online)2023

Effects of the surface irregularity of termite tunnels on food transport efficiency: a simulation study.

Sang-Hee Lee, Cheol-Min Park, Sang-Bin Lee

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Article in Journal of insect science (Online), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Sang-Hee LeeDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon 34047, South Korea.ORCID 0000-0003-2708-071X
Cheol-Min ParkDivision of Industrial Mathematics, National Institute for Mathematical Sciences, Daejeon 34047, South Korea.
Sang-Bin LeeFt. Lauderdale Research and Education Center, Department of Entomology and Nematology, University of Florida, Ft. Lauderdale, FL 33314, USA.ORCID 0000-0001-7982-0842

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Termites are believed to have evolved in a way that optimizes their foraging efficiency, which involves both searching for food and transporting it efficiently. Although the search efficiency has been well-studied through tunnel pattern analysis, transport efficiency has received limited attention due to the challenges of directly observing behavior that is highly influenced by environmental conditions. In this study, we introduce an individual-based model to simulate transport behavior and examine transport efficiency (E) by considering the tunnel surface irregularities and curvature, which are critical environmental factors. The model is characterized by four control variables: tunnel curvature (k1), termite stopping time at irregularity sites (k2), irregularity distribution (k3), and irregularity density (k4). The simulation results indicate that as k1 increases, E decreases, while k3 has little impact on E. The impact of k4 on E is decisive; when k4 ≤ 6, an increase in k4 results in increased traffic jam frequency and a faster reduction in E. However, when k4 > 6, the jamming frequency is not significantly affected, reducing the decrease in E. k2 strongly contributes to reducing E without significantly affecting the frequency. In the discussion section, we explore potential mechanisms that termites use to maintain transport efficiency in heterogeneous soils, and discuss how to improve the model to better reflect real-termite systems.

Indexed as

IsopteraAnimalsBehavior, AnimalFoodSoilSoilfood transport efficiencyindividual-based modeltermitetraffic jamtunnel irregularity

Identifiers

PMID36947034
PMCPMC10032299

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LicenceCC BY
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measurements read3
Read underepoch 390

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.